Similarly, the describe-to-detect approach, e.g. D2D [1]}, also postpones the detection to a later stage but applies such detector on pre-learned dense descriptors to extract a sparse set of keypoints and corresponding descriptors. Dense feature extraction foregoes the detection stage and performs the description stage densely across the whole image [2]}, [3]}, [4]}, [5]}. In practice, this approach has shown to lead to better matching results than sparse feature matching, particularly under strong variations in illumination [6]}, [7]}. Different from these works, which purely rely on image features, 2D3D-MatchNet [8]} proposed to learn local descriptors that allow direct matching of key points across a 2D image and 3D point cloud. Similarly, LCD [9]} introduced a dual auto-encoder architecture to extract cross-domain local descriptors. However, they still require pre-defined 2D and 3D keypoints separately, which will result in poor matching results caused by inconsistent keypoint selection rules.
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